M-tracking for space-time imaging

ABSTRACT

A new method is introduced able to track the motion of a thin object, e.g. a wall, from a series of digital field data (e.g. images) in order to analyze field information corresponding to the moving position of the object. The method is based on the extraction of M-mode images where the wall can be identified and the wall properties can be extracted and analyzed. The digital implementation of the method into electronic equipments improves the quality of the information that can be extracted from field data and the potential diagnostic capability when applied to echographic medical imaging.

TECHNICAL FIELD

Automatic processing of two- and three-dimensional time-varying digitaldata analysis, primarily pertinent to medical imaging (e.g. echographicimages) to improve the ability of extracting specific tissue-relatedinformation.

BACKGROUND ART

In many practical, clinical and industrial applications it is oftenrequested the evaluation of properties in correspondence of a specificelement of a system and the quantification of the variation of suchproperties with time. The application that stimulated the development ofthe present method derives from capability of tracking the movingvascular wall (e.g. myocardium) from cardiovascular images in such a wayto extract from the image the properties (velocity, brightness, etc.)evaluated in correspondence of the tissue.

One driving example is the analysis of the time-course of blood flow inthe capillary bed of the myocardium, based on the echo-contrast imaging.The health of the myocardium can be established, among other ways, fromthe ability of the blood to reach all muscular cells; if a myocardialregion is not perfused then derives mechanical failure (e.g. duringangina or myocardial infarction). It is therefore important to developtechniques able to evaluate the perfusion properties of the differenttissue regions. The quantification of myocardial perfusion is made byintroducing a contrast agent in vein, it moves with the blood andquantification of its presence in the myocardial tissue is equivalent toquantification of myocardial perfusion (Becher and Burns 2000). Theanalysis is made utilizing the ability of ultrasound machine to detectthe echo enhancement deriving from the contrast medium that perfuses themyocardium. Recent examples of quantitative analysis of segmentalperfusion are reported in literature (Nor-Avi et al. 1993; Wei et al.1998; Masugata et al. 2001).

Crucial for an adequate quantification of the contrast signal is theability to follow the systolic and diastolic movement of the heartwalls. With the respect of the ultrasound probe, the heart show not onlyinherent movement but also displacements due to respiration. Moreover,the physician performing the examination can move the probe itselfduring the acquisition of the data. For these reasons, if we try toregister the signal of the wall utilizing a region of interest (ROI)placed at a fixed location, frequently, the ROI fall on other structures(like left or the right ventricular cavities or outside the heart). Forthese reasons, only if the wall is continuously tracked we can extractthe signal originating from the tissue and not outside of it and soextract quantitative parameters of regional perfusion.

Such an approach has a widespread application not only inechocardiography (e.g perfusion study analysis, regional wall velocityanalysis and quantification, computation of segmental strain and strainrate (Heimdal et al. 1998; Voigt et al. 2000)) but also in industrialapplications when the tracking of a moving material is necessary and insome applications of visual recognition by intelligent electronicdevices.

Currently, the quantification of wall-related properties is performedsimply by analyzing the properties within a ROI, (sometimes just a fewpixels within the image) selected well inside the myocardial tissue. Itis then important to verify that the selected ROI remains inside thetissue in all the images of the sequence otherwise information that donot pertain to the tissue are included and the analysis is corrupted. Tomake sure that we aren't introducing erroneous samples in the dataset,the sequence should be reviewed frame by frame: when the ROI fallsoutside the tissue it must be moved manually on the wall. It is evidenthow such an approach is inherently extremely time-consuming (in mostcase for each ROI we must review more than 100 frames and a competeevaluation requires to analyze up to 20 different ROI). Sometimes thisprocedure can performed automatically with methods that depend from thesoftware available. In most cases, these are based on standard edgedetection algorithm or on cross-correlation alignment methods (Pratt1991), however these technique do not guarantee the accuracy of theresults that must be still verified manually because they incorporate noinformation about the structure and the geometry, of the object thatmust be recognized.

We present here a novel method that allows to continuously tracks intime the wall contained inside a two or three-dimensional representation(images) that is well suited for the case when the wall is relativelythin. After the wall is recognized it is straightforward to analyze thetime evolution of properties in correspondence of the detected wall.

DISCLOSURE OF THE INVENTION

Consider a two-dimensional image containing a non-uniform elongatedregion corresponding to the part of the tissue that we want to analyze.One example in reported in FIG. 1 where an image of the left ventricleis show as taken from echographic machine during contrast analysis. Thewall is not immediately recognizable as a well-defined structureseparated from the background; nevertheless it is possible to evaluateproperties (e.g. brightness of the image that is proportional to theamount of contrast inside the myocardium, in the example of FIG. 1) incorrespondence of the tissue.

It is common practice to select a region that is instantaneously overthe wall (FIG. 2), and to control in the following images if it remainsover it. Differently an automatic procedure can be developed as follows.

Consider one segment (or a series of them), that crosses the wall alongthe whole thickness, i.e. a transmural cut, being sure that the segmentis long enough and starts and ends outside the wall during all instantsas shown in the image of FIG. 2. The distribution of the sought property(e.g. brightness) along the single line can be represented for allinstants at once in a two-dimensional representation (often referred asM-mode representation) where one axis is the distance along the line andthe other axis is the time. An example of such a representation is shownin FIG. 3 (above).

Once a M-mode representation is obtained the matter of defining the wallbecomes substantially facilitated. In fact the wall should now besought, at each instant, simply along a single line, where its center isdefined by one single point and the wall edge (in those cases when anedge can be defined) is defined by two points as shown in FIG. 3(below). The center of the wall can be defined with statistical methodsby the center of the distribution of the brightness or darkness (oranother desired quantity in different applications). The wall thicknesscan be sought by standard one-dimensional edge finding methods or,statistically, by the dispersion of the wall related property.

This method for tissue tracking is easily translated into an automaticprocedure to be included into a software application for analysis oftissue-related properties:

-   -   1. Consider a sequence of N digital images, read from a storage        medium, made of a number M of pixels (i.e. M is the product of        the number of rows and of columns in structured images), and        each image contains one datum for one specific quantity (e.g.        brightness in contrast echography, velocity in Doppler images)        such that q(i,k) is the digital value of such a quantity in the        i^(th) pixel of the k^(th) image;    -   2. The manual drawing of a line (or a curve) over one image        corresponds to selecting a group of M′ pixels inside the images,        whose indexes can be identified by one integer array ig of        dimension Ml;    -   3. The M-more representation is a new single array qm of        dimension M′□N such that qm(i,k)=q(ig(i),k);    -   4. The sought tissue properties are evaluated at the k^(th)        instant by analysing the single value function qm( . . . ,k)        from which it is straightforward to extract any properly defined        quantity with methods taken from statistical analysis, specific        weighted integrals, or from one dimensional edge detection        techniques.

Results can also be averaged from a series of M-modes in order toanalyze a finite segment of tissue.

Different examples of the potential outputs are:

-   -   a Define the tissue center (e.g. function barycentre) and its        thickness (e.g. function standard deviation), and evaluate the        time evolution of the property value integrated over the tissue        thickness. One example of this application is shown in FIG. 4.    -   b. The unbiased time-averaged spatial profile of the tissue        property can be evaluated, after a tissue center is defined, by        performing averages keeping the center of tissue aligned during        time. One example of this application is shown in FIG. 5.

The accuracy of this method is inherently related to the quality of theimage itself, nevertheless this new approach is optimal in the case ofnoisy images, like the ones shown in the figures, because transforms thetwo- or three-dimensional problem into a series of one-dimensional ones.The three-dimensional case does not differ conceptually from thetwo-dimensional one although its software implementations includes morecomplex management steps based on voxels in place of pixels.

This method can be inserted into software applications for differentspecific purposes to improve the accuracy of analysis concerning tissuerelated properties. The routine for wall tracking has been included intotwo different sofware applications for myocardial perfusion and fortissue strain analysis. Different versions of these software, withdifferent language implementations, have been prepared to run onpersonal computers where digital images, read from echographs, areavailable. The software has been extensively tested in a series ofapplications about Tissue Doppler analysis and cardiac echo-contrastexperiments with clinical cases to verify its applicability. The resultsconfirm the physical consistency and accuracy of the method.

BEST MODE FOR CARRYING OUT THE INVENTION

The procedure resulting from the serial combination of the passagesoutlined above is a method that allows to identify the wall, or anotherinhomogeneous region, on the basis of the normal data measured fromechographs and commonly represented as image. Implementation of thistechnique must be based on a numerical analysis (software application),therefore it should be supported in digital processing by an electronicinstrument.

The procedure outlined above can be implemented with most programminglanguages, and the routine included inside a complete softwareapplication for the analysis of tissue related properties. The softwarecan be an internal one running on echographic machines or can beexecuted on an external computer equipped to read the echographic data.

The direct inclusion into echographs is more appropriate in the case ofan application for rapid analyses that can be performed during thenormal echograph use. The inclusion into an external device is moreappropriate in the case of software dedicated to more accurate analysisthat are cornnonly performed offline. In both cases the equipmentbecomes a system with the capability of automatic wall recognition andis able to evaluate the wall-related properties thus minimizing the riskof taking data that do not compete to the tissue.

The implementation of the new approach improves the quality of theinformation that can be obtained from the electronic equipment, thusgiving an additional potential feature that is potentially relevant forseveral diagnostic applications.

Cited References

-   1. H. Becher and P. Burns, Handbook of Contrast Echocardiography:    Left Ventricular Function and Myocardial Perfusion. Springer, 2000.-   2. A. Heimdal, A Stoylen, H. Torp, T. Skjwrpe, “Real-Time Strain    Rate Imaging of the Left Ventricle by Ultrasound”, J Am Soc    Echocardiogr vol. 11 pp. 1013-9, 1998.-   3. H. Masugata, B. Peters, S. Lafitte, G. M. Strachan, K.    Ohmori, A. N. DeMaria, “Quantitative assessment of myocardial    perfusion during graded coronary stenosis by real-time myocardial    contrast echo refilling curves”, J Am Coll Cardiol, vol. 37, pp.    262-9, 2001.-   4. V. Mor-Avi, D. David, S. Akselrod, Y. Bitton, I. Choshniak,    “Myocardial regional blood flow: quantitative measurement by    computer analysis of contrast enhanced echocardiographic images”,    Ultrasound Med Biol vol. 19 pp. 619-33, 1993.-   5. W. K. Pratt, Digital Image Processing, 2nd edition. Wiley, 1991.-   6. J.-U. Voigt, M. F. Arnold, M. Karlsson, L. Hubbert, T.    Kukulski, L. Hatle, G. R. Sutherland, “Assessment of Regional    Longitudinal Myocardial Strain Rate Derived from Doppler Myocardial    Imaging Indexes in Normal and Infarcted Myocardium”, J Am Soc    Echocardiogr vol. 13 pp. 588-98,2000.-   K. Wei, A. R. Jayaweera, S. Firoozan, A. Linka, D. M. Skyba, S.    Kaul, “Quantification of myocardial blood flow with    ultrasound-induced destruction of microbubbles administered as a    constant venous infusion”, Circulation vol. 97 pp. 473-83, 1998.

1. A method for the automatic detection of a moving tissue region from asequence of echographic images, or 3D data; characterized by the stepof: scanning a series of transmural cuts crossing said tissue region andconstructing corresponding space-time images of said transmural cuts;analyzing each of said space-time images to form information indicativeof said tissue moving along said transmural cuts; and processing saidinformation to define said moving tissue region.
 2. A method forevaluating a property of a moving tissue region characterized by thesteps of: scanning a series of transmural cuts crossing said tissueregion and constructing corresponding space-time images of saidtransmural cuts; analyzing each of said space-time images to forminformation of said tissue moving along said transmural cuts; extractingproperty from said information corresponding, to said moving tissue; andaveraging the results of said extracting to reconstruct said propertyfor said moving tissue region.
 3. A method for evaluating a time-averagerepresentation of a property of a moving tissue region characterized bythe step of: scanning a series of transmural cuts crossing said tissueregion and constructing corresponding space-time images of saidtransmural cuts; analyzing each of said space-time images to forminformation of said tissue moving along sad transmural cuts; aligningsaid space-time images to eliminate or reduce the appearance of tissuemotion; assigrning said aligned space-time images to appropriate pixelvalue along said transmural cuts; and combining said pixel values foreach of said transmural cuts to obtain a representation of said movingtissue region.
 4. A method as claimed in claims 1, applied to a singlethree-dimensional set of data by means of substituting a time coordinatewith one spatial coordinate.
 5. A method as claimed in claims 1, appliedto a sequence of three-dimensional sets of data.
 6. A method as claimedin claims 1, applied to medical Tissue Doppler Imaging.
 7. A method asclaimed in claims 1, applied to medical Magnetic Resonance Imaging.
 8. Amethod as claimed in claim 1, applied to non medical imaging thatrequires representation of a non-uniform elongated moving region.
 9. Amethod as claimed in claim 2, applied to a single three-dimensional setof data by means of substituting a time coordinate with one spatialcoordinate.
 10. A method as claimed in claim 3, applied to a singlethree-dimensional set of data by means of substituting a time coordinatewith one spatial coordinate.
 11. A method as claimed in claim 2, appliedto a sequence of three-dimensional sets of data
 12. A method as claimedin claim 3, applied to a sequence of three-dimensional sets of data. 13.A method as claimed in claim 2, applied to medical Tissue DopplerImaging.
 14. A method as claimed in claim 3, applied to medical TissueDoppler Imaging.
 15. A method as claimed in claim 2, applied to medicalMagnetic Resonance Imaging.
 16. A method as claimed in claim 3, appliedto medical Magnetic Resonance Imaging.
 17. A method as claimed in claim2 applied to non medical imaging that requires representation of anon-uniform elongated moving region.
 18. A method as claimed in claim 3applied to non medical imaging that requires representation of anon-uniform elongated moving region.